113 citations · 146 across the 7 of their papers we have counts for
13 papers · 1 filter
Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution
Toan Q. Nguyen, Kenton Murray, David Chiang
In this paper, we investigate the driving factors behind concatenation, a simple but effective data augmentation method for low-resource neural machine translation. Our experiments…
Efficiency through Auto-Sizing: Notre Dame NLP's Submission to the WNGT 2019 Efficiency Task
Kenton Murray, Brian DuSell, David Chiang
This paper describes the Notre Dame Natural Language Processing Group's (NDNLP) submission to the WNGT 2019 shared task (Hayashi et al., 2019). We investigated the impact of auto-s…
Auto-Sizing the Transformer Network: Improving Speed, Efficiency, and Performance for Low-Resource Machine Translation
Kenton Murray, Jeffery Kinnison, Toan Q. Nguyen +2
Neural sequence-to-sequence models, particularly the Transformer, are the state of the art in machine translation. Yet these neural networks are very sensitive to architecture and…
Correcting Length Bias in Neural Machine Translation
Kenton Murray, David Chiang
We study two problems in neural machine translation (NMT). First, in beam search, whereas a wider beam should in principle help translation, it often hurts NMT. Second, NMT has a t…
Neural Machine Translation of Text from Non-Native Speakers
Antonios Anastasopoulos, Alison Lui, Toan Nguyen +1
Neural Machine Translation (NMT) systems are known to degrade when confronted with noisy data, especially when the system is trained only on clean data. In this paper, we show that…
Part-of-Speech Tagging on an Endangered Language: a Parallel Griko-Italian Resource
Antonis Anastasopoulos, Marika Lekakou, Josep Quer +3
Most work on part-of-speech (POS) tagging is focused on high resource languages, or examines low-resource and active learning settings through simulated studies. We evaluate POS ta…